The Experts below are selected from a list of 9201 Experts worldwide ranked by ideXlab platform
Alan J. Barton - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry
2007 International Joint Conference on Neural Networks, 2007Co-Authors: Julio J. Valdes, Alan J. BartonAbstract:A method for the construction of virtual reality spaces for visual Data Mining using multi-objective optimization with genetic algorithms on neural networks is presented. Two neural network layers (output and last hidden) are used for the construction of simultaneous solutions for: a supervised classification of Data patterns and the computation of two unsupervised similarity structure preservation measures between the original Data matrix and its image in the new space. A set of spaces is constructed from selected solutions along the Pareto front which enables the understanding of the internal properties of the Data based on visual inspection of non-dominating spaces with different properties. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. The presented approach is domain independent and is illustrated with an Application to the study of hydrochemical properties of ice and water samples from the Arctic.
Julio J. Valdes - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry
2007 International Joint Conference on Neural Networks, 2007Co-Authors: Julio J. Valdes, Alan J. BartonAbstract:A method for the construction of virtual reality spaces for visual Data Mining using multi-objective optimization with genetic algorithms on neural networks is presented. Two neural network layers (output and last hidden) are used for the construction of simultaneous solutions for: a supervised classification of Data patterns and the computation of two unsupervised similarity structure preservation measures between the original Data matrix and its image in the new space. A set of spaces is constructed from selected solutions along the Pareto front which enables the understanding of the internal properties of the Data based on visual inspection of non-dominating spaces with different properties. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. The presented approach is domain independent and is illustrated with an Application to the study of hydrochemical properties of ice and water samples from the Arctic.
Vahida Attar - One of the best experts on this subject based on the ideXlab platform.
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soil Data analysis using classification techniques and soil attribute prediction
arXiv: Artificial Intelligence, 2012Co-Authors: Jay Gholap, Anurag Ingole, Jayesh Gohil, Shailesh Gargade, Vahida AttarAbstract:Agricultural research has been profited by technical advances such as automation, Data Mining. Today ,Data Mining is used in a vast areas and many off-the-shelf Data Mining system products and domain specific Data Mining Application soft wares are available, but Data Mining in agricultural soil Datasets is a relatively a young research field. The large amounts of Data that are nowadays virtually harvested along with the crops have to be analyzed and should be used to their full extent. This research aims at analysis of soil Dataset using Data Mining techniques. It focuses on classification of soil using various algorithms available. Another important purpose is to predict untested attributes using regression technique, and implementation of automated soil sample classification.
T. Bright - One of the best experts on this subject based on the ideXlab platform.
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Creating a text Data-Mining Application for use in public health informatics
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004Co-Authors: M.c. Michel, L.a. Bero, T. BrightAbstract:Recent litigation and the Master Settlement Agreement of 1998 have made millions of tobacco industry internal documents available on the Internet (http://legacy.library.ucsf.edu). The Legacy interface, housed at the University of California, San Francisco, is based on a traditional information retrieval model in which documents are indexed and retrieved based on user-specified queries. One problem with the Legacy interface is information overload. In an attempt to ease this problem, we are developing a text-Mining interface to enable exploratory analysis and discovery of information from collections of Data. Users could uncover new patterns and concepts and thus text Mining could result in searches that are targeted and specific, which would decrease information overload. In order to determine information needs, nine in-depth interviews with regular users of the Legacy interface were conducted. Results show that participants identified clustering as a useful tool in identifying and extracting key concepts and identified the need to recognize relationships between terms and concepts within the Data. We encourage researchers who are developing text-Mining interfaces to survey the users to learn what particular aspects of their research could be enhanced by text Mining.
Eun Whan Lee - One of the best experts on this subject based on the ideXlab platform.
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Data Mining Application in customer relationship management for hospital inpatients
Healthcare Informatics Research, 2012Co-Authors: Eun Whan LeeAbstract:OBJECTIVES: This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a Data Mining Application in customer relationship management (CRM) for hospital inpatients. METHODS: A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. RESULTS: Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. CONCLUSIONS: To discover a hospital's loyal patients and model their medical usage patterns, the Application of Data-Mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with Data-Mining in CRM.
